Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
multilingual
Portuguese
English
bert
bert-base-multilingual-cased
semantic role labeling
finetuned
Instructions to use liaad/srl-en_mbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liaad/srl-en_mbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liaad/srl-en_mbert-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-en_mbert-base") model = AutoModel.from_pretrained("liaad/srl-en_mbert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd8438658a124fbd988b7c3d61fba5fcd5304c48d404a93d1936e7ab1480b341
- Size of remote file:
- 711 MB
- SHA256:
- a6775dd3469c6cf9f1f3174909b5893fc10f17994153974786987ad741f7fac6
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